Seismic datasets often contain complex patterns that are subtle and manifested in multiple seismic or attribute/derivative volumes and at multiple spatial scales. Over the last several decades, geologists and geophysicists have developed a range of techniques to extract many important patterns that indicate the presence of hydrocarbons. However, most of these methods involve searching for either known or loosely defined patterns with pre-specified characteristics in one data volume, or two volumes at the most. These “template-based” or “model-based” approaches often miss subtle or unexpected anomalies that do not conform to such specifications. These approaches will not be discussed further here as they have little in common with the present invention except that they address the same technical problem. It is therefore desirable to develop statistical analysis methods that are capable of automatically highlighting anomalous regions in one or more volumes of seismic data across multiple spatial scales without a priori knowledge of what they are and where they are.
PCT Patent Publication WO 2010/056424 discloses a method to perform such statistical analysis to highlight anomalies automatically in multi-volume seismic analysis using a single moving window on the data to gather statistics. See also PCT Patent Publication WO 2011/139416. Both of these patent application publications are incorporated by reference herein in all countries that allow it. However, the single-windowed approach has some limitations arising from a lack of adaptivity, which biases the results towards prominent detection of obvious and dominant anomalies with weaker or suppressed response for subtle anomalies. Accordingly, there is a need for a method that mitigates these limitations, and the present invention satisfies this need.